System and Method for Diagnosing a Biological Sample

ABSTRACT

The present disclosure provides for a system and method for analyzing biological samples to thereby provide a diagnosis. A system may comprise an illumination source, a filter and a detector configured to generate at least one of: a visible data set representative of a biological sample, a SWIR data set representative of a biological sample, and combinations thereof. A method may comprise illuminating a biological sample to generate a plurality of photons, filtering a said plurality of interacted photons, detecting

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/225,005, filed on Sep. 2, 2011, entitled “System and Method forDiagnosing a Biological Sample.” This Application is also acontinuation-in-part to pending U.S. patent application Ser. No.12/188,796, filed on Aug. 8, 2008, entitled “Raman Difference SpectraBased Disease Classification,” which itself claims priority under 35U.S.C. §119(e) to U.S. Provisional Patent Application No. 60/954,607,filed on Aug. 8, 2007, entitled “Gleason Score Based Cancer TissueAnalysis.” These patent applications are hereby incorporated byreference in their entireties.

BACKGROUND

The biochemical composition of a cell is a complex mix of biologicalmolecules including, but not limited to, proteins, nucleic acids,lipids, and carbohydrates. The composition and interaction of thebiological molecules determines the metabolic state of a cell. Themetabolic state of the cell will dictate the type of cell and itsfunction (i.e., red blood cell, epithelial cell, etc.). Tissue isgenerally understood to mean a group of cells that work together toperform a function. Spectroscopic techniques provide information aboutthe biological molecules contained in cells and tissues and thereforeprovide information about the metabolic state. As the cell's or tissue'smetabolic state changes from the normal state to a diseased state,spectroscopic techniques can provide information to indicate themetabolic change and therefore serve to diagnose and predict the outcomeof a disease. Cancer is a prevalent disease, so physicians are veryconcerned with being able to accurately diagnose cancer and to determinethe best course of treatment.

Spectroscopic imaging combines digital imaging and molecularspectroscopy techniques, which can include Raman scattering,fluorescence, photoluminescence, ultraviolet, visible, short waveinfrared (SWIR), and infrared absorption spectroscopies. When applied tothe chemical analysis of materials, spectroscopic imaging is commonlyreferred to as chemical imaging. Instruments for performingspectroscopic (i.e. chemical) imaging typically comprise an illuminationsource, image gathering optics, focal plane array imaging detectors andimaging spectrometers.

In general, the sample size determines the choice of image gatheringoptic. For example, a microscope is typically employed for the analysisof sub micron to millimeter spatial dimension samples. For largerobjects, in the range of millimeter to meter dimensions, macro lensoptics are appropriate. For samples located within relativelyinaccessible environments, flexible fiberscope or rigid borescopes canbe employed. For very large scale objects, such as planetary objects,telescopes are appropriate image gathering optics.

For detection of images formed by the various optical systems,two-dimensional, imaging focal plane array (FPA) detectors are typicallyemployed. The choice of FPA detector is governed by the spectroscopictechnique employed to characterize the sample of interest. For example,silicon (Si) charge-coupled device (CCD) detectors or CMOS detectors aretypically employed with visible wavelength fluorescence and Ramanspectroscopic imaging systems, while indium gallium arsenide (InGaAs)FPA detectors are typically employed with near-infrared spectroscopicimaging systems.

Spectroscopic imaging of a sample can be implemented by one of twomethods. First, a point-source illumination can be provided on thesample to measure the spectra at each point of the illuminated area.Second, spectra can be collected over the an entire area encompassingthe sample simultaneously using an electronically tunable opticalimaging filter such as an acousto-optic tunable filter (AOTF), amulti-conjugate tunable filter (MCF), or a liquid crystal tunable filter(LCTF). Here, the organic material in such optical filters are activelyaligned by applied voltages to produce the desired bandpass andtransmission function. The spectra obtained for each pixel of such animage thereby forms a complex data set referred to as a hyperspectralimage which contains the intensity values at numerous wavelengths or thewavelength dependence of each pixel element in this image.

The ability to determine a disease state is critical to histologicalanalysis. Such testing often requires obtaining the spectrum of a sampleat different wavelengths. Conventional spectroscopic devices operateover a limited range of wavelengths due to the operation ranges of thedetectors or tunable filters possible. This enables analysis in theUltraviolet (UV), visible (VIS), near infrared (NIR), short waveinfrared (SWIR) mid infrared (MIR) wavelengths and to some overlappingranges. These correspond to wavelengths of about 180-380 nm (UV),380-700 nm (VIS), 700-2500 nm (NIR), 850-1700 nm (SWIR) and 2500-25000nm (MIR).

Various types of spectroscopy and imaging may be explored for detectionof various types of diseases in particular cancers. Raman spectroscopyis based on irradiation of a sample and detection of scatteredradiation, and it can be employed non-invasively to analyze biologicalsamples in situ. Thus, little or no sample preparation is required.Raman spectroscopy techniques can be readily performed in aqueousenvironments because water exhibits very little, but predictable, Ramanscattering. It is particularly amenable to in vivo measurements as thepowers and excitation wavelengths used are non-destructive to the tissueand have a relatively large penetration depth.

Chemical imaging is a reagentless tissue imaging approach based on theinteraction of laser light with tissue samples. The approach yields animage of a sample wherein each pixel of the image is the spectrum of thesample at the corresponding location. The spectrum carries informationabout the local chemical environment of the sample at each location. Forexample, Raman chemical imaging (RCI) has a spatial resolving power ofapproximately 250 nm and can potentially provide qualitative andquantitative image information based on molecular composition andmorphology.

The vast majority of diseases, in particular cancer cases, arepathologically diagnosed using tissue from a biopsy specimen. Thereforeit is desirable to devise systems and methodologies that usespectroscopic techniques to diagnose biological samples.

SUMMARY OF THE INVENTION

The present disclosure provides for a system and method for assessingbiological samples. More specifically, the invention of the presentdisclosure provides for the use of SWIR and/or visible spectroscopic andimaging techniques to diagnose biological samples. This diagnosis mayinclude, but is not limited to, determining at least one of: a diseasestate, a metabolic state, a clinical outcome, a disease progression, andcombinations thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic representation of an exemplary system of thepresent disclosure.

FIGS. 2A and 2B are schematic representations of an exemplaryspectroscopy module of the present disclosure.

FIG. 3 is a schematic representation of an exemplary system of thepresent disclosure.

FIG. 4 is illustrative of a method of the present disclosure.

FIG. 5 is illustrative of a method of the present disclosure.

FIG. 6A is illustrative of the detection capabilities of the system andmethod of the present disclosure.

FIG. 6B is illustrative of the detection capabilities of the system andmethod of the present disclosure.

FIG. 6C is illustrative of the detection capabilities of the system andmethod of the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to the preferred embodiments of thepresent disclosure, examples of which are illustrated in theaccompanying drawings. Wherever possible, the same reference numberswill be used throughout the specification to refer to same or likeparts.

FIG. 1 illustrates an exemplary system 100 according to one embodimentof the present disclosure. System 100 includes a spectroscopy module 110in communication with a processing module 120. Processing module 120 mayinclude a processor 122, databases 123, 124, 125 and 126, and machinereadable program code 128. The machine readable program code 128 maycontain executable program instructions, and the processor 122 may beconfigured to execute the machine readable program code 128 so as toperform the methods of the present disclosure. In one embodiment, theprogram code 128 may contain the ChemImage Xpert™ software marketed byChemImage Corporation of Pittsburgh, Pa. The Xpert™ software may be usedto process spectroscopic data and information received from thespectroscopy module 110 to obtain various spectral plots and images, andto also carry out various multivariate image analysis methods discussedlater herein below.

FIG. 2A illustrates an exemplary schematic layout of the spectroscopymodule 110 shown in FIG. 1. The layout in FIG. 2A may relate to achemical imaging system marketed by ChemImage Corporation of Pittsburgh,Pa. In one embodiment, the spectroscopy module 110 may include amicroscope module 140 containing optics for microscope applications. Anillumination source 142 (e.g., a laser illumination source) may provideilluminating photons to a sample (not shown) handled by a samplepositioning unit 144 via the microscope module 140. In one embodiment,photons transmitted, reflected, emitted, or scattered from theilluminated sample (not shown) may pass through the microscope module(as illustrated by exemplary blocks 146, 148 in FIG. 2A) before beingdirected to one or more of spectroscopy or imaging optics in thespectroscopy module 110. In the embodiment of FIG. 2A, visible imaging154 and SWIR imaging 152 are illustrated as standard. In otherembodiments, the modes of Raman imaging 158, fluorescence imaging 156,NIR imaging 151, and video imaging 150 may also be implemented.

The spectroscopy module 110 may also include a control unit 160 tocontrol operational aspects (e.g., focusing, sample placement, laserbeam transmission, etc.) of various system components including, forexample, the microscope module 140 and the sample positioning unit 144as illustrated in FIG. 2A. In one embodiment, operation of variouscomponents (including the control unit 160) in the spectroscopy module110 may be fully automated or partially automated, under user control.

It is noted here that in the discussion herein the terms “illumination,”“illuminating,” “irradiation,” and “excitation” are used interchangeablyas can be evident from the context. For example, the terms “illuminationsource,” “light source,” and “excitation source” are usedinterchangeably. Similarly, the terms “illuminating photons” and“excitation photons” are also used interchangeably. Furthermore,although the discussion hereinbelow focuses more on visible and SWIRspectroscopy and imaging, various methodologies discussed herein may beadapted to be used in conjunction with other types of spectroscopyapplications as can be evident to one skilled in the art based on thediscussion provided herein.

FIG. 2B illustrates exemplary details of the spectroscopy module 110 inFIG. 2A according to one embodiment of the present disclosure.Spectroscopy module 110 may operate in several experimental modes ofoperation including bright field reflectance and transmission imaging,polarized light imaging, differential interference contrast (DIC)imaging, UV induced autofluorescence imaging, NIR imaging, wide fieldillumination whole field Raman spectroscopy, wide field spectralfluorescence imaging, wide field visible imaging, wide field SWIRimaging, wide field visible imaging, and wide field spectral Ramanimaging. Module 110 may include collection optics 203, light sources 202and 204, and a plurality of spectral information processing devicesincluding, for example: a tunable fluorescence filter 222, a tunableRaman filter 218, a dispersive spectrometer 214, a plurality ofdetectors including a fluorescence detector 224, and Raman detectors 216and 220, a fiber array spectral translator (“FAST”) device 212, filters208 and 210, and a polarized beam splitter (PBS) 219.

A FAST device may comprise a two-dimensional array of optical fibersdrawn into a one-dimensional fiber stack so as to effectively convert atwo-dimensional field of view into a curvilinear field of view, andwherein said two-dimensional array of optical fibers is configured toreceive said photons and transfer said photons out of said fiber arrayspectral translator device and to at least one of: a spectrometer, afilter, a detector, and combinations thereof.

The FAST device can provide faster real-time analysis for rapiddetection, classification, identification, and visualization of, forexample, explosive materials, hazardous agents, biological warfareagents, chemical warfare agents, and pathogenic microorganisms, as wellas non-threatening objects, elements, and compounds. FAST technology canacquire a few to thousands of full spectral range, spatially resolvedspectra simultaneously, This may be done by focusing a spectroscopicimage onto a two-dimensional array of optical fibers that are drawn intoa one-dimensional distal array with, for example, serpentine ordering.The one-dimensional fiber stack may be coupled to an imagingspectrometer, a detector, a filter, and combinations thereof. Softwaremay be used to extract the spectral/spatial information that is embeddedin a single CCD image frame.

One of the fundamental advantages of this method over otherspectroscopic methods is speed of analysis. A complete spectroscopicimaging data set can be acquired in the amount of time it takes togenerate a single spectrum from a given material. FAST can beimplemented with multiple detectors. Color-coded FAST spectroscopicimages can be superimposed on other high-spatial resolution gray-scaleimages to provide significant insight into the morphology and chemistryof the sample.

The FAST system allows for massively parallel acquisition offull-spectral images. A FAST fiber bundle may feed optical informationfrom is two-dimensional non-linear imaging end (which can be in anynon-linear configuration, e.g., circular, square, rectangular, etc.) toits one-dimensional linear distal end. The distal end feeds the opticalinformation into associated detector rows. The detector may be a CCDdetector having a fixed number of rows with each row having apredetermined number of pixels. For example, in a 1024-width squaredetector, there will be 1024 pixels (related to, for example, 1024spectral wavelengths) per each of the 1024 rows.

The construction of the FAST array requires knowledge of the position ofeach fiber at both the imaging end and the distal end of the array. Eachfiber collects light from a fixed position in the two-dimensional array(imaging end) and transmits this light onto a fixed position on thedetector (through that fiber's distal end).

Each fiber may span more than one detector row, allowing higherresolution than one pixel per fiber in the reconstructed image. In fact,this super-resolution, combined with interpolation between fiber pixels(i.e., pixels in the detector associated with the respective fiber),achieves much higher spatial resolution than is otherwise possible.Thus, spatial calibration may involve not only the knowledge of fibergeometry (i.e., fiber correspondence) at the imaging end and the distalend, but also the knowledge of which detector rows are associated with agiven fiber.

In one embodiment, a system of the present disclosure may comprise FASTtechnology available from ChemImage Corporation, Pittsburgh, Pa. Thistechnology is more fully described in the following U.S. Patents, herebyincorporated by reference in their entireties: U.S. Pat. No. 7,764,371,filed on Feb. 15, 2007, entitled “System And Method For Super ResolutionOf A Sample In A Fiber Array Spectral Translator System”; U.S. Pat. No.7,440,096, filed on Mar. 3, 2006, entitled “Method And Apparatus ForCompact Spectrometer For Fiber Array Spectral Translator”; U.S. Pat. No.7,474,395, filed on Feb. 13, 2007, entitled “System And Method For ImageReconstruction In A Fiber Array Spectral Translator System”; and U.S.Pat. No. 7,480,033, filed on Feb. 9, 2006, entitled “System And MethodFor The Deposition, Detection And Identification Of Threat Agents UsingA Fiber Array Spectral Translator”.

In another embodiment, the system of FIG. 2B may be configured tooperate in a visible and/or SWIR configuration. In such an embodiment,fluorescence CCD 224, Raman dispersive CCD 216, and/or Raman imaging CCD220 may be replaced with at least one of a visible CCD, a SWIR CCD andcombinations thereof. The present disclosure contemplates that otherdetectors may be used including, but not limited to: ICCD detectors,CMOS detectors, geranium detectors, InGaAs detectors, and combinationsthereof. In such a visible and/or SWIR configuration, the FILCTF 222and/or Raman LCTF 218 may be replaced with at least one of a visibleLCTF, a SWIR LCTF, and combinations thereof. The present disclosure alsocontemplates that other tunable filters may also be used, including butnot limited to: a liquid crystal tunable filter, a multi-conjugatetunable filter, an acousto-optical tunable filter, a Lyot liquid crystaltunable filter, an Evans split-element liquid crystal tunable filter, aSolc liquid crystal tunable filter, a ferroelectric liquid crystaltunable filter, a Fabry Perot liquid crystal tunable filter, andcombinations thereof.

In one embodiment, the processor 122 (FIG. 1) may be operatively coupledto light sources 202 and 204, and the plurality of spectral informationprocessing devices 214, 218 and 222. In another embodiment, theprocessor 122 (FIG. 1), when suitably programmed, can configure variousfunctional parts of the spectroscopy module in FIG. 1 and may alsocontrol their operation at run time. The processor, when suitablyprogrammed, may also facilitate various remote data transfer andanalysis operations discussed in conjunction with FIG. 3. Module 110 mayoptionally include a video camera 205 for video imaging applications.Although not shown in FIG. 2B, spectroscopy module 110 may include manyadditional optical and electrical components to carry out variousspectroscopy and imaging applications supported thereby.

A sample 201 may be placed at a focusing location (e.g., by using thesample positioning unit 144 in FIG. 2A) to receive illuminating photonsand to also provide reflected, emitted, scattered, or transmittedphotons from the sample 201 to the collection optics 203. Sample 201 mayinclude a variety of biological samples. In one embodiment, the sample201 includes at least one cell or a tissue containing a plurality ofcells. The sample may contain normal (non-diseased or benign) cells,diseased cells (e.g., cancerous tissues with or without a progressivecancer state or malignant cells with or without a progressive cancerstate) or a combination of normal and diseased cells. In one embodiment,the cell/tissue is a mammalian cell/tissue. Some examples of biologicalsamples may include prostate cells, kidney cells, lung cells, coloncells, bone marrow cells, brain cells, red blood cells, and cardiacmuscle cells. In one embodiment, the biological sample may includeprostate cells. In one such embodiment, the biological sample mayinclude Gleason 6 prostate cells: In another such embodiment, thebiological sample may include Gleason 7 prostate cells. In anotherembodiment the biological sample may include a renal sample. In one suchembodiment, the biological sample may include renal oncocytoma cells. Inanother such embodiment, the biological sample may include chromophoberenal carcinoma. In another embodiment, the sample 201 may include cellsof plants, non-mammalian animals, fungi, protists, and monera. In yetanother embodiment, the sample 201 may include a test sample (e.g., abiological sample under test to determine its metabolic state or itsdisease status or to determine whether it is cancerous state wouldprogress to the next level). The “test sample,” “target sample” orunknown sample are used interchangeably herein to refer to a biologicalsample or renal sample under investigation, wherein such interchange usemay be without reference to such biological sample's metabolic state ordisease status.

A progressive cancer state is a cancer that will go on to becomeaggressive and acquire subsequent treatment by more aggressive means inorder for the patient to survive. An example of progressive cancer is aGleason score 7 cancer found in a prostate which has been surgicallyremoved, where the patient, subsequent to the removal of the prostate,develops metastatic cancer. In this example the cancer progressed evenafter the removal of the source organ. Progressive cancers can bedetected and identified in other organs and different types of cancer.

A non-progressive cancer is a cancer that does not progress to moreadvanced disease, requiring aggressive treatment. Many prostate cancersare non-progressive by this definition because though they are cancer bystandard histopathological definition, they do not impact the life ofthe patient in a way that requires significant treatment. In many casessuch cancers are observed and treated only if they show evidence ofbecoming progressive. Again, this is not a state particular to prostatecancer. Cancer cells are present in tissues of many health people.Because these do not ever transition to a state where they becomeprogressive in terms of growth, danger to the patient, or inconvenienceto the patient they would be considered non-progressive as the term isused herein.

The designation of progressive vs. non progressive can also be extendedto other disease or metabolic states. As an example, diabetes can beclinically described as “stable”, “well managed” by a clinician andwould fall into the non-progressive class. In contrast diabetes can beprogressing through the common course of the disease with all of theeffects on kidneys, skin, nerves, heart and other organs which are partof the disease. As a second example multiple sclerosis is a diseasewhich exists in many people is a stable, non-progressive state. In somepeople the disease rapidly progresses through historically observedpattern of physical characteristics with clinical manifestations.

The cells can be isolated cells, such as individual blood cells or cellsof a solid tissue that have been separated from other cells of thetissue (e.g., by degradation of the intracellular matrix). The cells canalso be cells present in a mass, such as a bacterial colony grown on asemi-solid medium or an intact or physically disrupted tissue. By way ofexample, blood drawn from a human can be smeared on the surface of asuitable substrate (e.g., an aluminum-coated glass slide) and individualcells in the sample can be separately imaged by light microscopy andSWIR and/or visible analysis using the spectroscopy module 110 of FIG.2B. Similarly a slice of a solid tissue (e.g., a piece of fresh tissueor a paraffin-embedded thin section of a tissue) can be imaged on asuitable surface.

The cells can be cells obtained from a subject (e.g., cells obtainedfrom a human blood or urine sample, semen sample, tissue biopsy, orsurgical procedure). Cells can also be studied where they naturallyoccur, such as cells in an accessible location (e.g., a location on orwithin a human body), cells in a remote location using a suitable probe,or by revealing cells (e.g., surgically) that are not normallyaccessible.

Referring again to FIG. 2B, light source 202 may be used to irradiatethe sample 201 with substantially monochromatic light. Light source 202can include any conventional photon source, including, for example, alaser, an LED (light emitting diode), or other IR (infrared) or near IR(NIR) devices. The substantially monochromatic radiation reaching sample201 illuminates the sample 201, and may produce photons scattered fromdifferent locations on or within the illuminated sample 201. A portionof the Raman scattered photons from the sample 201 may be collected bythe collection optics 203 and directed to dispersive spectrometer 214 orRaman tunable filter 218 for further processing discussed later hereinbelow. In one embodiment, light source 202 includes a laser light sourceproducing light at 532.1 nm. The laser excitation signal is focused onthe sample 201 through combined operation of reflecting mirrors M1, M2,M3, the filter 208, and the collection optics 203 as illustrated by anexemplary optical path in the embodiment of FIG. 2B. The filter 208 maybe tilted at a specific angle from the vertical (e.g., at 6.5.sup.0) toreflect laser illumination onto the mirror M3, but not to reflectRaman-scattered photons received from the sample 201. The other filter210 may not be tilted (i.e., it remains at 0.sup.0 from the vertical).Filters 208 and 210 may function as laser line rejection filters toreject light at the wavelength of laser light source 202.

In the spectroscopy module 110 in the embodiment of FIG. 2B, the secondlight source 204 may be used to irradiate the sample 201 withultraviolet light or visible light. In one embodiment, the light source204 includes a mercury arc (Hg arc) lamp that produces ultravioletradiation (UV) having wavelength at 365 nm for fluorescence spectroscopyapplications. In yet another embodiment, the light source 204 mayproduce visible light at 546 nm for visible light imaging applications.A polarizer or neutral density (ND) filter with or without a beamsplitter (BS) may he provided in front of the light source 204 to obtaindesired illumination light intensity and polarization.

In the embodiment of FIG. 2B, the dispersive spectrometer 214 and theRaman tunable filter 218 function to produce test data sets of sample201. A test data set, in one embodiment, may correspond to one or moreof the following: a plurality of Raman spectra of the sample; and aplurality of spatially accurate wavelength resolved Raman images of thesample. In one embodiment, the plurality of Raman spectra is generatedby dispersive spectral measurements of individual cells. In thisembodiment, the illumination of the individual cell may cover the entirearea of the cell so the dispersive Raman spectrum is an integratedmeasure of spectral response from all the locations within the cell.

In another embodiment, the Raman data set corresponds to a threedimensional block of Raman data (e.g., a spectral hypercube or a Ramanimage) having spatial dimensional data represented in the x and ydimensions and wavelength data represented in the z dimension. EachRaman image has a plurality of pixels where each has a corresponding xand y position in the Raman image. The Raman image may have one or moreregions of interest. The regions of interest may be identified by thesize and shape of one or more pixels and is selected where the pixelsare located within the regions of interest. A single Raman spectrum isthen extracted from each pixel located in the region of interest,leading to a plurality of Raman spectra for each of the regions ofinterest. The extracted plurality of Raman spectra are then designatedas the Raman data set. In this embodiment, the plurality of Ramanspectra and the plurality of spatially accurate wavelength resolvedRaman images are generated, as components of the hypercube, by acombination of the Raman tunable filter 218 and Raman imaging detector220 or by a combination of the FAST device 212, the dispersivespectrometer 214, and the Raman detector 216.

In another embodiment, configured for visible and/or SWIR analysis of abiological sample, a test data set may correspond to one or more of thefollowing: a plurality of visible spectra of the sample, a plurality ofSWIR spectra of the sample, a plurality of spatially accurate wavelengthresolved visible images of the sample, a plurality of spatially accuratewavelength resolved SWIR images of the sample, and combinations thereof.In one embodiment, a plurality of spectra may be generated by dispersivespectral measurements of individual cells. In this embodiment, theillumination of the individual cell may cover the entire area of thecell so the dispersive spectrum is an integrated measure of spectralresponse from all the locations within the cell.

In another embodiment, the test data set corresponds to a threedimensional block of data (e.g., a spectral hypercube or a visible orSWIR image) having spatial dimensional data represented in the x and ydimensions and wavelength data represented in the z dimension. Eachvisible or SWIR image has a plurality of pixels where each has acorresponding x and y position in the image. The visible or SWIR imagemay have one or more regions of interest. The regions of interest may beidentified by the size and shape of one or more pixels and is selectedwhere the pixels are located within the regions of interest. A singlespectrum is then extracted from each pixel located in the region ofinterest, leading to a plurality of spectra for each of the regions ofinterest. The extracted plurality of spectra is then designated as thetest data set. In this embodiment, the plurality of spectra and theplurality of spatially accurate wavelength resolved images aregenerated, as components of the hypercube, by a combination of a tunablefilter and imaging detector or by a combination of the FAST device, adispersive spectrometer, and a detector.

In yet another embodiment, a Raman dataset is generated using a Ramanimage to identify one or more regions of interest of the sample 201. Inone such embodiment, the one or more regions of interest contain atleast one of the following: an epithelium area, a stroma area,epithelial-stromal junction (ESJ) area and/or nuclei area. A pluralityof Raman spectra may be obtained from the one or more of regions ofinterest of the sample 201. In standard operation the Raman spectrumgenerated by selecting a region of interest in a Raman image is theaverage spectrum of all the spectra at each pixel within the region ofinterest. The standard deviation between of all the spectra in theregion of interest may be displayed along with the average Ramanspectrum of the region of interest. Alternatively, all of the spectraassociated with pixels within a region can be considered as a pluralityof spectra, without the step of reducing them to a mean and standarddeviation.

With further reference to FIG. 2B, the fluorescence tunable filter 222may function to produce fluorescence data sets of the photons emittedfrom the sample 201 under suitable illumination (e.g., UV illumination).In one embodiment, the fluorescence data set includes a plurality offluorescence spectra of sample 201 and/or a plurality of spatiallyaccurate wavelength resolved fluorescence images of sample 201. Afluorescence spectrum of sample 210 may contain a fluorescence emissionsignature of the sample 201. In one embodiment, the emission signaturemay be indicative of a fluorescent probe (e.g., fluoresceinisothiocyanate) within the sample 201. The fluorescence data sets may bedetected by fluorescence CCD detector 224. A portion of the fluorescenceemitted photons or visible light reflected photons from the sample 201may be directed to the video imaging camera 205 via a mirror M4 andappropriate optical signal focusing mechanism.

In one embodiment, a microscope objective (including the collectionoptics 203) may be automatically or manually zoomed in or out to obtainproper focusing of the sample.

The entrance slit (not shown) of the spectrometer 214 may be opticallycoupled to the output end of the fiber array spectral translator device212 to disperse the Raman scattered photons received from the FASTdevice 212 and to generate a plurality of spatially resolved Ramanspectra from the wavelength-dispersed photons. The FAST device 212 mayreceive Raman scattered photons from the beam splitter 219, which maysplit and appropriately polarize the Raman scattered photons receivedfrom the sample 201 and transmit corresponding portions to the input endof the FAST device 212 and the input end of the Raman tunable filter218.

Referring again to FIG. 2B, the tunable fluorescence filter 222 and thetunable Raman filter 218 may be used to individually tune specificphoton wavelengths of interest and to thereby generate a plurality ofspatially accurate wavelength resolved spectroscopic fluorescence imagesand Raman images, respectively, in conjunction with correspondingdetectors 224 and 220. In one embodiment, each of the fluorescencefilter 222 and the Raman filter 218 includes a two-dimensional tunablefilter, such as, for example, an electro-optical tunable filter, aliquid crystal tunable filter (LCTF), or an acousto-optical tunablefilter (AOTF). A tunable filter may be a band-pass or narrow band filterthat can sequentially pass or “tune” fluorescence emitted photons orRaman scattered photons into a plurality of predetermined wavelengthbands. The plurality of predetermined wavelength bands may includespecific wavelengths or ranges of wavelengths. In one embodiment, thepredetermined wavelength bands may include wavelengths characteristic ofthe sample undergoing analysis. The wavelengths that can be passedthrough the fluorescence filter 222 and Raman filter 218 may range from200 nm (ultraviolet) to 2000 nm (i.e., the far infrared). The choice ofa tunable filter depends on the desired optical region and/or the natureof the sample being analyzed. Additional examples of a two-dimensionaltunable filter may include a Fabry Perot angle tuned filter, a Lyotfilter, an Evans split element liquid crystal tunable filter, a Solcliquid crystal tunable filter, a spectral diversity filter, a photoniccrystal filter, a fixed wavelength Fabry Perot tunable filter, anair-tuned Fabry Perot tunable filter, a mechanically-tuned Fabry Perottunable filter, and a liquid crystal Fabry Perot tunable filter. Asnoted before, the tunable filters 218, 222 may be selected to operate inone or more of the following spectral ranges: the ultraviolet (UV),visible, and near infrared. In one such embodiment, the tunable filters218, 222 may be selected to operate in spectra ranges of 900-1155cm−.sup.1 and 15-30-1850 cm−.sup.1 Raman shift values.

In one embodiment, a multi-conjugate filter (MCF) may be used instead ofa simple LCTF (e.g., the LCTF 218 or 222) to provide more precisewavelength tuning of photons received from the sample 201. Someexemplary multi-conjugate filters are discussed, for example, in U.S.Pat. No. 6,992,809, titled “Multi-Conjugate Liquid Crystal TunableFilter;” and in the United States Published Patent Application NumberUS2007/0070260A1, titled “Liquid Crystal Filter with Tunable RejectionBand,” the disclosures of both of these publications are incorporatedherein by reference in their entireties.

In the embodiment of FIG. 2B, the fluorescence spectral data sets(output from the tunable filter 222) may be detected by the detector224, and the Raman spectral data sets (output from the spectrometer 214and the tunable filter 218) may be detected by detectors 216 and 220.The detectors 216, 220, and 224 may detect received photons in aspatially accurate manner. Detectors 216, 220 and 224 may include anoptical signal (or photon) collection device such as, for example, animage focal plane array (FPA) detector, a charge coupled device (CCD)detector, or a CMOS (Complementary Metal Oxide Semiconductor) arraysensor. Detectors 216, 220 and 224 may measure the intensity ofscattered, transmitted or reflected light incident upon their sensingsurfaces (not shown) at multiple discrete locations or pixels, andtransfer the spectral information received to the processor module 120for storage and analysis. The optical region employed to characterizethe sample of interest governs the choice of two-dimensional arraydetector. For example, a two-dimensional array of silicon charge-coupleddevice (CCD) detection elements can be employed with visible wavelengthemitted or reflected photons, or with Raman scatter photons, whilegallium arsenide (GaAs) and gallium indium arsenide (InGaAs) FPAdetectors can be employed for image analyses at near infraredwavelengths. The choice of such devices may also depend on the type ofsample being analyzed.

In one embodiment, a display unit (not shown) may be provided to displayspectral data collected by various detectors 216, 220, 224 in apredefined or user-selected format. The display unit may be a computerdisplay screen, a display monitor, an LCD (liquid crystal display)screen, or any other type of electronic display device.

Referring again to FIG. 1, the databases 123-126 may store variousreference spectral data sets including, for example, a reference SWIRdata set, a reference visible data set, a reference Raman data set, areference fluorescence data set, a reference NIR data set, etc. Thereference data sets may be collected from different samples and may beused to detect or identify the sample 201 from comparison of itsspectral data set with the reference data sets. In one embodiment,during operation, the test data sets of the sample 201 also may bestored in one or more of the databases (e.g., database 123) of theprocessing module 120.

For example, in one embodiment, database 123 may be used to store aplurality of reference data sets from reference cells having a knowndiagnosis, such as metabolic state or disease state. In one suchembodiment, the reference data sets may correspond to a plurality ofreference spectra. In another such embodiment, the Raman data sets maycorrespond to a plurality of reference spatially accurate wavelengthresolved images.

In another embodiment, the database 124 may be used to store a firstplurality of reference data sets from reference normal (non-diseased)cells. In one embodiment, the first reference set of data sets mayinclude a plurality of first reference spectra. In another embodiment, afirst reference spectrum may correspond to a dispersive spectrum. In afurther embodiment, the first reference set of data sets may include aplurality of first reference spatially accurate wavelength resolvedimages obtained from corresponding reference normal cells.

In yet another embodiment, the database 125 may store a second pluralityof reference data sets from different types of reference diseased cells.In one such embodiment, the reference diseased cells correspond tochromophobe renal carcinoma cells. In one embodiment, the secondreference set of data sets includes a plurality of second referencespectra. In one embodiment, the second reference spectrum may correspondto a dispersive spectrum. In another embodiment, the second referenceset of data sets may include a plurality of second reference spatiallyaccurate wavelength resolved images obtained from correspondingreference diseased cells.

Similarly, database 126 may store a plurality of reference SWIR and/orvisible spectra and/or a plurality of reference spatially accuratewavelength resolved SWIR and/or visible spectroscopic images obtainedfrom reference biological samples (e.g., cancerous human tissues). Oneor more of the reference biological samples may include probe molecules(e.g., fluorescein isothiocyanate). In one embodiment, a single databasemay be used to store all types of spectra.

The reference data sets may be associated with a reference image. In onesuch embodiment, the reference image may include at least one of: a SWIRimage, a visible image, a brightfield image; a polarized light image;and a UV-induced autofluorescence image.

FIG. 3 depicts an exemplary setup to remotely perform spectroscopicanalysis of test samples according to one embodiment of the presentdisclosure. Spectroscopic data from a test sample or a test sample maybe collected at a data generation site 260 using a spectroscopy module265. In one embodiment, the spectroscopy module may be functionallysimilar to the spectroscopy module 110 discussed hereinbefore withreference to FIGS. 2A-2B. The spectroscopic data collected at the datageneration site 260 may be transferred to a data analysis site 270 via acommunication network 272. In one embodiment, the communication network272 may be any data communication network such as an Ethernet LAN (localarea network) connecting all the data processing and computing unitswithin a facility, e.g., a university research laboratory, or acorporate research center. In that case, the data generation site 260and the data analysis site 270 may be physically located within the samefacility, e.g., a university research laboratory or a corporate researchcenter. In alternative embodiments, the communication network 272 mayinclude, independently or in combination, any of the present or futurewireline or wireless data communication networks such as, for example,the Internet, the PSTN (public switched telephone network), a cellulartelephone network, a WAN (wide area network), a satellite-basedcommunication link, a MAN (metropolitan area network), etc. In thiscase, the data generation site 260 and the data analysis site 270 may bephysically located in different facilities. In some embodiments, thedata generation site 260 and the data analysis site 270 that are linkedby the communication network 272 may be owned or operated by differententities.

The data analysis site 270 may include a processing module 275 toprocess the spectroscopic data received from the data generation site260. In one embodiment, the processing module 275 may be similar to theprocessing module 120 and may also include a number of differentdatabases (not shown) storing different reference spectroscopic datasets (e.g., a first plurality of reference data sets for non-progressivecancer tissues, a second plurality of reference data sets forprogressive cancer tissues, a third plurality of reference data sets fornormal or non-diseased tissues; a fourth plurality of reference data setfor renal oncocytomas samples and chromophobe renal cell carcinomasamples, etc.). The processing module 275 may include a processor(similar to the processor 122 of the processing module 120 in FIG. 1)that is configured to execute program code or software to performvarious spectral data processing tasks according to the teachings of thepresent disclosure. The machine-readable program code containingexecutable program instructions may be initially stored on a portabledata storage medium, e.g., a floppy diskette 294, a compact disc or aDVD 295, a data cartridge tape (not shown), or any other suitabledigital data storage medium. The processing module 275 may includeappropriate disk drives to receive the portable data storage medium andmay be configured to read the program code stored thereon, therebyfacilitating execution of the program code by its processor. The programcode, upon execution by the processor of the processing module 275, maycause the processor to perform a variety of data processing and displaytasks including, for example, initiate transfer of spectral data setfrom the data generation site 260 to the data analysis site 270 via thecommunication network 272, compare the received spectral data set tovarious reference data sets stored in the databases of the processingmodule 275, classify or identify the test sample based on the comparison(e.g., whether the test sample has a progressive cancer ornon-progressive cancer state or whether the test sample has renaloncocytomas disease or chromophobe renal cell carcinoma disease),transfer the classification or identification results to the datageneration site 260 via the communication network 272, etc.

In one embodiment, the data analysis site 270 may include one or morecomputer terminals 286A-286C communicatively connected to the processingmodule 275 via corresponding data communication links 290A-290C, whichcan be serial, parallel, or wireless communication links, or a suitablecombination thereof. Thus, users may utilize functionalities of theprocessing module 275 via their computer terminals 286A-286C, which mayalso be used to display spectroscopic data received from the datageneration site 260 and the results of the spectroscopic data processingby the processing module 275, among other applications. It is evidentthat in a practical application, there may be many more computerterminals 286 than just three terminals shown in FIG. 3.

The computer terminals 286A-286C may be, e.g., a personal computer (PC),a graphics workstation, a multiprocessor computer system, a distributednetwork of computers, or a computer chip embedded as part of a machineor mechanism. Similarly, the data generation site 260 may include one ormore of such computers (not shown) for viewing the results of thespectroscopic analysis received from the data analysis site 270. Eachcomputer terminal, whether at the data generation site 260 or at thedata analysis site 270, may include requisite data storage capability inthe form of one or more volatile and non-volatile memory modules. Thememory modules may include RAM (random access memory), ROM (read onlymemory) and HDD (hard disk drive) storage.

It is noted that the arrangement depicted in FIG. 3 may be used toprovide a commercial, network-based spectroscopic data processingservice that may perform customer-requested processing of spectroscopicdata in real time or near real time. For example, the processing module275 at the data analysis site 270 may be configured to identify a testsample from the spectroscopic data remotely submitted to it over thecommunication network 272 (e.g., the Internet) from the spectroscopymodule 265 automatically or through an operator at the data generationsite 260. The client site (data generation site) 260 may be, forexample, a government laboratory or a medical facility or pathologicallaboratory. The results of spectroscopic data analysis may betransmitted back to the client site 260 for review and further analysis.In one embodiment, the whole data submission, analysis, and reportingprocess can be automated.

It is further noted that the owner or operator of the data analysis site270 may commercially offer a network-based spectroscopic data contentanalysis service, as illustrated by the arrangement in FIG. 3, tovarious individuals, corporations, governmental entities, laboratories,or other facilities on a fixed-fee basis, on a per-operation basis or onany other payment plan mutually convenient to the service provider andthe service recipient.

Processing module 120 may also include a test database associated with atest biological sample having an unknown metabolic state. In one suchembodiment, the test data set may correspond to a plurality of SWIRand/or visible spectra of the test biological sample. In another suchembodiment, the test data set may correspond to a plurality of spatiallyaccurate wavelength resolved SWIR and/or visible images of the testbiological sample. In another embodiment, each of the test SWIR and/orvisible data sets may be associated with least one of the following: acorresponding test SWIR image; a corresponding test visible image; andanother corresponding test image. In one such embodiment, this othertest image may include at least one of the following: a brightfieldimage; a polarized light image; and a UV-induced autofluorescence image.

In one such embodiment, processing module 120 may also include a testdatabase associated with a test biological sample having an unknowndiagnosis. In one such embodiment, the test data set may correspond to aplurality of SWIR and/or visible spectra of the test biological sample.In another such embodiment, the test data set may correspond to aplurality of spatially accurate wavelength resolved SWIR images of thetest biological sample. In another embodiment, the test data set maycorrespond to a plurality of spatially accurate wavelength resolvedvisible images. In another embodiment, each of the test data sets may beassociated with least one of the following: a corresponding test SWIRimage; a corresponding test visible image; and another correspondingimage. In one such embodiment, the other image may include at least oneof the following: a brightfield image; a polarized light image; and aUV-induced autofluorescence image.

In one embodiment, the test spectra are generated using a test image toidentify one or more regions of interest of the test biological sample.In one such embodiment, the one or more regions of interest contain atleast one of the following: an epithelium area, a stroma area,epithelial-stromal junction (ESJ) area, and/or nuclei area. A pluralityof test spectra may be obtained from the one or more of regions ofinterest of the test biological sample.

A diagnosis of a test sample as diseased or non-diseased or a predictionof the metabolic state of a test sample may be made by comparing a testdata set to reference data sets using a chemometric technique. In oneembodiment, the chemometric technique may be spectral unmixing. Theapplication of spectral unmixing to determine the identity of componentsof a mixture is described in U.S. Pat. No. 7,072,770, entitled “Methodfor Identifying Components of a Mixture via Spectral Analysis, issued onJul. 4, 2006, which is incorporated herein by reference in it entirety.Spectral unmixing as described in the above referenced patent can beapplied as follows: Spectral unmixing requires a library of spectrawhich include possible components of the test sample. The library can inprinciple be in the form of a single spectrum for each component, a setof spectra for each component, a single SWIR and/or visible image foreach component, a set of SWIR and/or visible images for each component,or any of the above as recorded after a dimension reduction proceduresuch as Principle Component Analysis. In the methods discussed herein,the library used as the basis for application of spectral unmixing isthe reference data sets.

With this as the library, a set of measurements made on a sample ofunknown state, described herein as a test SWIR and/or visible data set,is assessed using the methods of U.S. Pat. No. 7,072,770 to determinethe most likely groups of components which are present in the sample. Inthis instance the components are actually disease states of interestand/or clinical outcome. The result is a set of disease state groupsand/or clinical outcome groups with a ranking of which are most likelyto be represented by the test data set.

Given a set of reference spectra, such as those described above, a pieceor set of test data can be evaluated by a process called spectralmixture resolution. In this process, the test spectrum is approximatedwith a linear combination of reference spectra with a goal of minimizingthe deviation of the approximation from the test spectrum. This processresults in a set of relative weights for the reference spectra.

In one embodiment, the chemometric technique may be Principal ComponentAnalysis. Using Principal Component Analysis results in a set ofmathematical vectors defined based on established methods used inmultivariate analysis. The vectors form an orthogonal basis, meaningthat they are linearly independent vectors. The vectors are determinedbased on a set of input data by first choosing a vector which describesthe most variance within the input data. This first “principalcomponent” or PC is subtracted from each of the members of the inputset. The input set after this subtraction is then evaluated in the samefashion (a vector describing the most variance in this set is determinedand subtracted) to yield a second vector—the second principal component.The process is iterated until either a chosen number of linearlyindependent vectors (PCs) are determined, or a chosen amount of thevariance within the input data is accounted for.

In one embodiment, the Principal Component Analysis may include a seriesof steps. A pre-determined vector space is selected that mathematicallydescribes a plurality of reference data sets. Each reference data setmay be associated with a known biological sample having an associatedmetabolic state. The test data set, may be transformed into thepre-determined vector space, and then a distribution of transformed datamay be analyzed in the pre-determined vector space to generate adiagnosis.

In another embodiment, the Principal Component Analysis may include aseries of steps. A pre-determined vector space is selected thatmathematically describes a first plurality of reference data setsassociated with a known biological sample having an associated diseasedstate and a second plurality of reference data sets associated with aknown biological sample having an associated non-diseased state. Thetest data set may be transformed into the pre-determined vector space,and then a distribution of transformed data may be analyzed in thepre-determined vector space to generate a diagnosis.

In yet another embodiment, the Principal Component Analysis may includea series of steps. A pre-determined vector space is selected thatmathematically describes a first plurality of reference data setsassociated with a known biological sample having an associatedprogressive state and a second plurality of reference data setsassociated with a known biological sample having an associatednon-progressive state. The test data set may be transformed into thepre-determined vector space, and then a distribution of transformed datamay be analyzed in the pre-determined vector space to generate adiagnosis.

In still yet another embodiment, the Principal Component Analysis mayinclude a series of steps. A pre-determined vector space may be selectedthat mathematically describes a first plurality of reference data setsassociated with a known diagnosis. The test data set may be transformedinto the pre-determined vector space, and then a distribution oftransformed data may he analyzed in the pre-determined vector space.

The analysis of the distribution of the transformed data may beperformed using a classification scheme. Some examples of theclassification scheme may include: Mahalanobis distance, Adaptivesubspace detector, Band target entropy method, Neural network, andsupport vector machine as an incomplete list of classification schemesknown to those skilled in the art.

In one such embodiment, the classification scheme is Mahalanobisdistance. The Mahalanobis distance is an established measure of thedistance between two sets of points in a multidimensional space thattakes into account both the distance between the centers of two groups,but also the spread around each centroid. A Mahalanobis distance modelof the data is represented by plots of the distribution of the spectrain the principal component space. The Mahalanobis distance calculationis a general approach to calculating the distance between a single pointand a group of points. It is useful because rather than taking thesimple distance between the single point and the mean of the group ofpoints, Mahalanobis distance takes into account the distribution of thepoints in space as part of the distance calculation. The Mahalanobisdistance is calculated using the distances between the points in alldimensions of the principal component space.

In one such embodiment, once the test data is transformed into the spacedefined by the predetermined PC vector space, the test data is analyzedrelative to the pre-determined vector space. This may be performed bycalculating a Mahalanobis distance between the test data set transformedinto the pre-determined vector space and the data sets in thepre-determined vector space to generate a diagnosis.

The exemplary systems of FIGS. 1 and 2 may be used to perform methods topredict the clinical outcome of patients or diagnose a disease state ofpatients. Processor 122 is configured to execute program instructions tocarry out these methods. In another embodiment of the presentdisclosure, the exemplary system of FIG. 3 may be used to carry outmethods to predict the clinical outcome of patients. In this method,data obtained at a data generation site is transmitted to an analysissite to obtain a prediction of the metabolic state of a test biologicalsample. The prediction is then transmitted back to the data generationsite. The transmission may be performed over a data communicationnetwork such as the Internet.

One embodiment of the present disclosure, illustrated by FIG. 4,provides for a method 400 for diagnosing a biological sample. In step410 a biological sample may be illuminated to thereby generate a firstplurality of interacted photons. In one embodiment, this illuminationmay be accomplished using wide-field illumination. In one embodiment, afirst plurality of interacted photons may be selected from the groupconsisting of: photons scattered by said biological sample, photonsreflected by said biological sample, photons absorbed by said biologicalsample, photons emitted by said biological sample, and combinationsthereof. In step 420 said first plurality of interacted photons may befiltered. In one embodiment, this filtering may further comprisefiltering said first plurality of interacted photons into a plurality ofpredetermined wavelength bands. In one embodiment, this filtering may beachieved using a filter selected from the group consisting of: a liquidcrystal tunable filter, a multi-conjugate tunable filter, anacousto-optical tunable filter, a Lyot liquid crystal tunable filter, anEvans split-element liquid crystal tunable filter, a Solc liquid crystaltunable filter, a ferroelectric liquid crystal tunable filter, a FabryPerot liquid crystal tunable filter, and combinations thereof.

A first plurality of interacted photons may be detected in step 430 tothereby generate a test data set representative of said biologicalsample. In one embodiment, this test data set may comprise at least oneof: a visible test data set, a SWIR test data set, and combinationsthereof.

In one embodiment, a test data set may comprise a hyperspectral image.In another embodiment, a test data set may comprise at least one of: aspatially accurate wavelength resolved image, a spectra, andcombinations thereof.

A test data set may be analyzed in step 440 to thereby diagnose saidbiological sample. In one embodiment, this diagnosis may comprise atleast one of: a disease state of said biological sample, a metabolicstate of said biological sample, a clinical outcome of said biologicalsample, a disease progression of said biological sample, andcombinations thereof. In one embodiment, diagnosing a biological samplemay further comprise assigning a Gleason score to said biologicalsample.

In another embodiment, the method 400 may further comprise selecting apre-determined vector space that mathematically describes said test dataset, transforming said test data set into said pre-determined vectorspace, and analyzing a distribution of said transformed test data set inthe pre-determined vector space to thereby diagnose said biologicalsample.

In one embodiment, the method 400 may further comprise providing adatabase wherein said database comprises at least one reference dataset, each reference data set associated with a known diagnosis. In oneembodiment, each reference data set may be associated with at least oneof: a known disease state, a known metabolic state, a known clinicaloutcome, a known disease progression, and combinations thereof. In suchan embodiment, the analyzing of step 440 may further comprise comparingsaid test data set to at least one reference data set. This comparisonmay be accomplished using a chemometric technique. This chemometrictechnique may be selected from the group consisting of: principlecomponents analysis, partial least squares discriminate analysis, cosinecorrelation analysis, Euclidian distance analysis, k-means clustering,multivariate curve resolution, band t. entropy method, mahalanobisdistance, adaptive subspace detector, spectral mixture resolution, andcombinations thereof.

In another embodiment, illustrated by FIG. 5, the present disclosureprovides for a method 500 for diagnosing a biological sample. In step510 a biological sample may be illuminated to thereby generate a firstplurality of interacted photons. In step 520 said first plurality ofinteracted photons may be filtered. These first plurality of interactedphotons may be detected in step 530 to thereby generate a test data setrepresentative of said biological sample. In one embodiment, this testdata set may comprise at least one of: a visible test data set, a SWIRtest data set, and combinations thereof. In step 540, said test data setmay be analyzed to thereby diagnose said biological sample, wherein saidanalyzing further comprises comparing said test data set to at least onereference data set using a chemometric technique.

The present disclosure contemplates the system and method disclosedherein may be used to analyze a variety of different types of biologicalsamples. In one embodiment, a biological sample may comprise a tissuesample, an organ sample, and combinations thereof. In anotherembodiment, a biological sample may comprise at least one cell. In oneembodiment, a biological sample may comprise at least one of: a kidneysample, a prostate sample, a breast sample, a pancreatic sample, a brainsample, a skin sample, an intestinal sample, a colon sample, a liversample, a cardiac sample, a lung sample, an esophageal sample, a bladdersample, a blood sample, a urethral sample, an ovarian sample, a uterinesample, a testicular sample, a bone sample, a stomach sample, a trachealsample, a tongue sample, a diaphragm sample, a nerve sample, a rectalsample, and combinations thereof.

The present disclosure also provides for a storage medium containingmachine readable program code, which when executed by a processor,causes the processor to perform the following: illuminate a biologicalsample to thereby generate a first plurality of interacted photons,filter said first plurality of interacted photons to thereby separatesaid first plurality of interacted photons into a plurality ofpredetermined wavelength bands, detect said first plurality ofinteracted photons to thereby generate a test data set representative ofsaid biological sample, wherein said test data set comprises at leastone of: a test SWIR data set, a test visible data set, and combinationsthereof, and analyze said test data set to thereby determine at leastone of: a disease state of said biological sample, a metabolic sate ofsaid biological sample, a clinical outcome of said biological sample, adisease progression of said biological sample, and combinations thereof.

In one embodiment, machine readable program code, when executed by aprocessor to analyze said test data, may further case said processor to:compare said test data set to at least one reference data set in areference database, wherein each said reference data set is associatedwith at least one of: a known disease state, a known metabolic state, aknown clinical outcome, a known disease progression, and combinationsthereof.

In one embodiment, machine readable program code, when executed by aprocessor to compare said test data set to at least one reference dataset, may further cause said processor to perform said comparison byapplying at least one chemometric technique. In another embodiment, saidtest data set may be compared to at least one reference data set tothereby assign a Gleason score to said biological sample.

FIGS. 6A-6C are illustrative of the detection capabilities of the systemand method of the present disclosure. FIG. 6A is a NIR chemical image(1450 nm) which shows contrast based on absorption. Regions of interest(ROIs) are indicated on the image, labeled as ROIs 1-5. FIG. 6B is adigital photograph of the tissue sample under analysis. FIG. 6C isillustrative of spectra corresponding to the various ROIs of FIG. 6A.Normalized image spectra from regions drawn on the image illustratecorrelation between spectral features and image contrast. These Figuresillustrate the potential of the system and method of the presentdisclosure for tissue analysis.

The present disclosure may be embodied in other specific forms withoutdeparting from the spirit or essential attributes of the disclosure.Accordingly, reference should be made to the appended claims, ratherthan the foregoing specification, as indicating the scope of thedisclosure. Although the foregoing description is directed to thepreferred embodiments of the disclosure, it is noted that othervariations and modification will be apparent to those skilled in theart, and may be made without departing from the spirit or scope of thedisclosure.

1. A method comprising: illuminating a biological sample to therebygenerate a first plurality of interacted photons; filtering said firstplurality of interacted photons; detecting said first plurality ofinteracted photons to thereby generate a test data set representative ofsaid biological sample, wherein said test data set comprises at leastone of: a test SWIR data set, a test visible data set, and combinationsthereof; and analyzing said test data set to thereby diagnose at leastone of: a disease state of said biological sample, a metabolic state ofsaid biological sample, a clinical outcome of said biological sample, adisease progression of said biological sample, and combinations thereof.2. The method of claim 1 wherein said first plurality of interactedphotons are selected from the group consisting of: photons scattered bysaid biological sample, photons reflected by said biological samplephotons absorbed by said biological sample, photons emitted by saidbiological sample, and combinations thereof.
 3. The method of claim 1wherein said biological sample comprises a tissue sample.
 4. The methodof claim 1 wherein said biological sample comprises an organ sample. 5.The method of claim 1 wherein said biological sample comprises at leastone cell.
 6. The method of claim 1 wherein said biological samplecomprises at least one of: a kidney sample, a prostate sample, a breastsample, a pancreatic sample, a brain sample, a skin sample, anintestinal sample, a colon sample, a liver sample, a cardiac sample, alung sample, an esophageal sample, a bladder sample, a blood sample, aurethral sample, an ovarian sample, a uterine sample, a testicularsample, a bone sample, a stomach sample, a tracheal sample, a tonguesample, a diaphragm sample, a nerve sample, a rectal sample, andcombinations thereof.
 7. The method of claim 1 wherein said test dataset comprises at least one hyperspectral SWIR image representative ofsaid biological sample.
 8. The method of claim 1 wherein said test dataset comprises at least one hyperspectral visible image representative ofsaid biological sample.
 9. The method of claim 1 wherein said test dataset comprises at least one of: a spatially accurate wavelength resolvedSWIR image, a SWIR spectrum, and combinations thereof.
 10. The method ofclaim 1 wherein said test data set comprises at least one of: aspatially accurate wavelength resolved visible image, a visiblespectrum, and combinations thereof.
 11. The method of claim 1 furthercomprising providing a reference database comprising a plurality ofreference data sets, each reference data set associated with at leastone of: a known disease state, a known metabolic state, a known clinicaloutcome, a known disease progression, and combinations thereof.
 12. Themethod of claim 1 wherein said analyzing further comprises comparingsaid test data set to at least one reference data set.
 13. The method ofclaim 12 wherein said comparing is achieved by applying at least onechemometric technique.
 14. The method of claim 13 wherein saidchemometric technique is selected from the group consisting of:principle components analysis, partial least squares discriminateanalysis, cosine correlation analysis, Euclidian distance analysis,k-means clustering, multivariate curve resolution, band t. entropymethod, mahalanobis distance, adaptive subspace detector, spectralmixture resolution, and combinations thereof.
 15. The method of claim 1wherein said illuminating comprises wide-field illumination.
 16. Themethod of claim 1 wherein said diagnosing further comprises assigning aGleason score to said biological sample.
 17. The method of claim 1further comprising selecting a pre-determined vector space thatmathematically describes said test data set; transforming said test dataset into said pre-determined vector space; and analyzing a distributionof said transformed test data set in the pre-determined vector space tothereby diagnose said biological sample.
 18. A system for analyzing abiological sample comprising: an illumination source, configured so asto illuminate a biological sample to thereby generate a first pluralityof interacted photons; a filter for filtering said first plurality ofinteracted photons into a plurality of predetermined wavelength bands; adetector for detecting said first plurality of interacted photons andgenerating a test data set representative of said sample.
 19. The systemof claim 18 wherein said detector comprises a focal plane arraydetector.
 20. The system of claim 19 wherein said focal plane arraydetector comprises at least one of: a CMOS detector, a CCD detector, anICCD detector, a germanium detector, a InGaAs detector, and combinationsthereof.
 21. The system of claim 18 wherein said filter comprises atunable filter.
 22. The system of claim 21 wherein said tunable filteris selected from the group consisting of: a liquid crystal tunablefilter, a multi-conjugate tunable filter, an acousto-optical tunablefilter, a Lyot liquid crystal tunable filter, an Evans split-elementliquid crystal tunable filter, a Solc liquid crystal tunable filter, aferroelectric liquid crystal tunable filter, a Fabry Perot liquidcrystal tunable filter, and combinations thereof.
 23. The system ofclaim 18 further comprising a fiber array spectral translator device.24. The system of claim 23 wherein said fiber array spectral translatordevice comprises a two-dimensional array of optical fibers drawn into aone-dimensional fiber stack so as to effectively convert atwo-dimensional field of view into a curvilinear field of view, andwherein said two-dimensional array of optical fibers is configured toreceive said photons and transfer said photons out of said fiber arrayspectral translator device and to at least one of: a filter, a detector,and combinations thereof.
 25. The system of claim 18 further comprisinga reference database comprising a plurality of reference data sets, eachreference data set associated with at least one of: a known diseasestate, a known metabolic state, a known clinical outcome, a knowndisease progression, and combinations thereof.
 26. The system of claim18 further comprising a means for comparing said test data set to atleast one reference data set in said reference database.
 27. The systemof claim 18 wherein said illumination source is configured forwide-field illumination.
 28. A storage medium containing machinereadable program code, which when executed by a processor, causes theprocessor to perform the following: illuminate a biological sample tothereby generate a first plurality of interacted photons; filter saidfirst plurality of interacted photons to thereby separate said firstplurality of interacted photons into a plurality of predeterminedwavelength bands; detect said first plurality of interacted photons tothereby generate a test data set representative of said biologicalsample, wherein said test data set comprises at least one of: a testSWIR data set, a test visible data set, and combinations thereof; andanalyze said test data set to thereby determine at least one of: adisease state of said biological sample, a metabolic state of saidbiological sample, a clinical outcome of said biological sample, adisease progression of said biological sample, and combinations thereof.29. The storage medium of claim 28 wherein said machine readable programcode, when executed by a processor to analyze said test data, furthercauses said processor to: compare said test data set to at least onereference data set in a reference database, wherein each said referencedata set is associated with at least one of: a known disease state, aknown metabolic state, a known clinical outcome, a known diseaseprogression, and combinations thereof.
 30. The storage medium of claim29 wherein said machine readable program code, when executed by aprocessor to compare said test data set to at least one reference dataset further causes said processor to perform said comparison by applyingat least one chemometric technique.
 31. The storage medium of claim 28wherein said machine readable program code, when executed by a processorfurther causes said processor to: compare said test data set to at leastone reference data set in a reference database to thereby assign aGleason score to said biological sample.